Drill wear monitoring and prediction has become one of the most important factors in achieving fully automated, high-quality manufacturing. By monitoring and predicting drill-bit wear, the useful life of each drill bit can be maximized. Further, as a consequence of the maximization of the useful life of the drill bit, the period between tool changes is likewise extended. Additionally, effective drill wear monitoring and prediction systems are able to quickly detect any complete failure of an associated drill bit during the manufacturing process.
Currently available methods for monitoring drill wear can be categorized into two groups. The first group utilizes signal amplitude analysis based upon the input of spindle motor current, feed force current, and force. The rationale behind the methods of the first group is that if the degree of drill wear is becoming severe, the amplitude of the spindle motor current and/or the feed force current will proportionately increase. These methods utilizing signal amplitude analysis work well under normal conditions, however, they require baseline knowledge about the drill bit sizes and the work piece types. Moreover, methods using signal amplitude analysis cannot properly track the degree of drill wear if the drilling operation starts with a partially worn drill.
The second group of drill wear monitoring methods utilizes acoustic emission signals instead of spindle motor and feed force currents. The acoustic emission signals are produced during the formation and growth of cracks in the workpiece or when corrosion occurs on the workpiece material. By sensing and analyzing the acoustic emission signals, it is possible to detect the changes in stress in a material. Drill wear monitoring utilizing acoustic emission signals have received considerable investigation, however, substantial obstacles to the development of an efficient system have been encountered. The primary obstacle results from the fact that the acoustic emissions typically occur in the frequency range of 100 KHz. At this frequency, high sampling rates and large memory are needed during real-time signal processing, which makes these methods impractical for applications requiring low cost processing equipment.
Thus, a need has arisen for an efficient means to monitor and predict drill bit wear in on-line machining applications. Such a means would not require intensive computations thereby allowing for the use of low cost processors. Further, such a means would not require the initial determination of either the drill size, type of workpiece material or the existing extent of the drill bit wear. Finally, the new means would eliminate the need for sophisticated analog signal conditioning necessary to condition the input for digital processing.